{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "635d8ebb",
   "metadata": {},
   "source": [
    "# Multi Agent Collaboration Network \n",
    "\n",
    "- Author: [Youngin Kim](https://github.com/Normalist-K)\n",
    "- Peer Review: \n",
    "- Proofread : [Chaeyoon Kim](https://github.com/chaeyoonyunakim)\n",
    "- This is a part of [LangChain Open Tutorial](https://github.com/LangChain-OpenTutorial/LangChain-OpenTutorial)\n",
    "\n",
    "[![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/LangChain-OpenTutorial/LangChain-OpenTutorial/blob/main/17-LangGraph/03-Use-Cases/06-LangGraph-Multi-Agent-Collaboration.ipynb)[![Open in GitHub](https://img.shields.io/badge/Open%20in%20GitHub-181717?style=flat-square&logo=github&logoColor=white)](https://github.com/LangChain-OpenTutorial/LangChain-OpenTutorial/blob/main/17-LangGraph/03-Use-Cases/06-LangGraph-Multi-Agent-Collaboration.ipynb)\n",
    "## Overview\n",
    "\n",
    "In this tutorial, we'll explore how to implement a **multi-agent network** using LangGraph.  \n",
    "A multi-agent network is an architecture that leverages a \"divide-and-conquer\" approach by breaking down complex tasks into smaller, specialized agents. \n",
    "\n",
    "This helps resolve the inefficiencies of a single agent managing numerous tools, allowing each agent to focus on its area of expertise and solve problems effectively.\n",
    "\n",
    "Inspired by the [AutoGen paper](https://arxiv.org/abs/2308.08155), this tutorial walks you through the process of building such multi-agent networks with LangGraph step-by-step. \n",
    "\n",
    "![network](./assets/17-langgraph-multi-agent-network.png)\n",
    "\n",
    "**Key Topics Covered**\n",
    "\n",
    "- **Defining States** : How to define graph states and manage the state information required for each agent's actions  \n",
    "- **Defining Tools** : How to specify tools that agents can use and add them as nodes  \n",
    "- **Creating Agents** : How to define agents and set them as nodes in a LangGraph network  \n",
    "- **Defining Agent Nodes** : How to define specialized agents as nodes  \n",
    "- **Defining Tool Nodes** : How to define tools as nodes and enable agents to utilize them  \n",
    "- **Defining Edge Logic** : How to set branching logic that directs agents or tools based on the results of an agent’s task  \n",
    "- **Graph Definition** : How to combine agents, tools, states, and edge logic into a cohesive graph  \n",
    "- **Running the Graph** : How to execute the graph and perform real-world tasks with the defined setup  \n",
    "\n",
    "Let's get started!\n",
    "\n",
    "\n",
    "### Table of Contents\n",
    "\n",
    "- [Overview](#overview)\n",
    "- [Environment Setup](#environment-setup)\n",
    "- [Why Do We Use Multi-Agent Networks?](#why-do-we-use-multi-agent-networks)\n",
    "- [Defining States](#defining-states)\n",
    "- [Defining Tools](#defining-tools)\n",
    "- [Creating Agents](#creating-agents)\n",
    "- [Building a Multi-Agent Graph](#building-a-multi-agent-graph)\n",
    "- [Running the Agents](#running-the-agents)\n",
    "\n",
    "### References\n",
    "\n",
    "- [Concept of Multi Agent Network LangGraph](https://langchain-ai.github.io/langgraph/concepts/multi_agent/#network)  \n",
    "- [AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversation (Wu et al.)](https://arxiv.org/abs/2308.08155)  \n",
    "----"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c6c7aba4",
   "metadata": {},
   "source": [
    "## Environment Setup\n",
    "\n",
    "Setting up your environment is the first step. See the [Environment Setup](https://wikidocs.net/257836) guide for more details.\n",
    "\n",
    "\n",
    "**[Note]**\n",
    "\n",
    "The langchain-opentutorial is a package of easy-to-use environment setup guidance, useful functions and utilities for tutorials.\n",
    "Check out the  [```langchain-opentutorial```](https://github.com/LangChain-OpenTutorial/langchain-opentutorial-pypi) for more details."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "21943adb",
   "metadata": {},
   "outputs": [],
   "source": [
    "%%capture --no-stderr\n",
    "%pip install langchain-opentutorial==0.0.4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "f25ec196",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Install required packages\n",
    "from langchain_opentutorial import package\n",
    "\n",
    "package.install(\n",
    "    [\n",
    "        \"langsmith\",\n",
    "        \"langchain\",\n",
    "        \"langchain_core\",\n",
    "        \"langchain_community\",\n",
    "        \"langchain_experimental\",\n",
    "        \"langchain_openai\",\n",
    "        \"langgraph\",\n",
    "        \"tavily-python\",  # for web search tool\n",
    "        \"matplotlib\",  # for visualize tool\n",
    "        \"pandas\",  # for visualize tool\n",
    "        \"numpy\",  # for visualize tool\n",
    "    ],\n",
    "    verbose=False,\n",
    "    upgrade=False,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "690a9ae0",
   "metadata": {},
   "source": [
    "You can set API keys in a ```.env``` file or set them manually.\n",
    "\n",
    "[Note] If you’re not using the ```.env``` file, no worries! Just enter the keys directly in the cell below, and you’re good to go."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "327c2c7c",
   "metadata": {},
   "outputs": [],
   "source": [
    "from dotenv import load_dotenv\n",
    "from langchain_opentutorial import set_env\n",
    "\n",
    "# Attempt to load environment variables from a .env file; if unsuccessful, set them manually.\n",
    "if not load_dotenv():\n",
    "    set_env(\n",
    "        {\n",
    "            \"OPENAI_API_KEY\": \"\",\n",
    "            \"TAVILY_API_KEY\": \"\",  # for tavily web search tool\n",
    "            \"LANGCHAIN_API_KEY\": \"\",\n",
    "            \"LANGCHAIN_TRACING_V2\": \"true\",\n",
    "            \"LANGCHAIN_ENDPOINT\": \"https://api.smith.langchain.com\",\n",
    "            \"LANGCHAIN_PROJECT\": \"Multi-Agent-Collaboration\",\n",
    "        }\n",
    "    )"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aa00c3f4",
   "metadata": {},
   "source": [
    "## Why Do We Use Multi-Agent Networks?\n",
    "\n",
    "A single agent can be efficient when working within a specific domain and using a limited number of tools. However, as the number of tools increases, several challenges arise:  \n",
    "1. The logic for managing tools becomes increasingly complex.  \n",
    "2. The amount of information the agent needs to process at once grows, leading to inefficiencies.\n",
    "\n",
    "By adopting a \"divide-and-conquer\" approach, each agent focuses on a specific task or area of expertise, while the overall workload is distributed across a network.  \n",
    "Each agent handles what it does best and delegates tasks to other specialized agents or utilizes tools effectively when needed.\n",
    "\n",
    "This structure enhances efficiency and simplifies the management of complex workflows."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "69cb77da",
   "metadata": {},
   "source": [
    "## Defining States\n",
    "\n",
    "```messages``` is a list of messages shared between agents, and ```sender``` refers to the sender of the most recent message."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "f2271570",
   "metadata": {},
   "outputs": [],
   "source": [
    "import operator\n",
    "from typing import Annotated, Sequence\n",
    "from typing_extensions import TypedDict\n",
    "from langchain_core.messages import BaseMessage\n",
    "\n",
    "\n",
    "# Defining state\n",
    "class AgentState(TypedDict):\n",
    "    messages: Annotated[\n",
    "        Sequence[BaseMessage], operator.add\n",
    "    ]  # a list of messages shared between agents\n",
    "    sender: Annotated[str, \"The sender of the last message\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ed901d72",
   "metadata": {},
   "source": [
    "## Defining Tools\n",
    "\n",
    "Here, we define a few tools that agents will use in the network:\n",
    "\n",
    "- ```TavilySearch``` : A tool for searching information on the internet. It is used by the ```research_agent``` to find the necessary data.  \n",
    "- ```PythonREPL``` : A tool for executing Python code. It is used by the ```chart_agent``` to create charts.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "4b95f9a4",
   "metadata": {},
   "outputs": [],
   "source": [
    "from typing import Annotated\n",
    "\n",
    "from langchain_community.tools.tavily_search import TavilySearchResults\n",
    "from langchain_core.tools import tool\n",
    "from langchain_experimental.utilities import PythonREPL\n",
    "\n",
    "# Defining tavily web search tool\n",
    "tavily_tool = TavilySearchResults(max_results=5)\n",
    "\n",
    "# Defining a tool for executing python code\n",
    "python_repl = PythonREPL()\n",
    "\n",
    "\n",
    "@tool\n",
    "def python_repl_tool(\n",
    "    code: Annotated[str, \"The python code to execute to generate your chart.\"],\n",
    "):\n",
    "    \"\"\"Use this to execute python code. If you want to see the output of a value,\n",
    "    you should print it out with `print(...)`. This is visible to the user.\"\"\"\n",
    "    try:\n",
    "        result = python_repl.run(code)\n",
    "    except BaseException as e:\n",
    "        return f\"Failed to execute code. Error: {repr(e)}\"\n",
    "\n",
    "    result_str = f\"Successfully executed:\\n```python\\n{code}\\n```\\nStdout: {result}\"\n",
    "\n",
    "    return (\n",
    "        result_str + \"\\n\\nIf you have completed all tasks, respond with FINAL ANSWER.\"\n",
    "    )"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "67a3687c",
   "metadata": {},
   "source": [
    "## Creating Agents\n",
    "\n",
    "Here’s an example of how to create agents. In this tutorial, we’ll use the Research Agent and the Chart Generator Agent to build a multi-agent network.\n",
    "\n",
    "\n",
    "First, define the LLM model and a common system prompt to be used for the agents."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "a839b339",
   "metadata": {},
   "outputs": [],
   "source": [
    "from langchain_openai import ChatOpenAI\n",
    "\n",
    "# Defining LLM\n",
    "llm = ChatOpenAI(model=\"gpt-4o\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "1d4d8c97",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Defining common system prompt\n",
    "def make_system_prompt(suffix: str) -> str:\n",
    "    return (\n",
    "        \"You are a helpful AI assistant, collaborating with other assistants.\"\n",
    "        \" Use the provided tools to progress towards answering the question.\"\n",
    "        \" If you are unable to fully answer, that's OK, another assistant with different tools \"\n",
    "        \" will help where you left off. Execute what you can to make progress.\"\n",
    "        \" If you or any of the other assistants have the final answer or deliverable,\"\n",
    "        \" prefix your response with FINAL ANSWER so the team knows to stop.\"\n",
    "        f\"\\n{suffix}\"\n",
    "    )"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ffcdd6a7",
   "metadata": {},
   "source": [
    "### Research Agent\n",
    "\n",
    "Create an agent that performs research using the ```TavilySearch``` tool. This agent is used to gather the required information."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "f3134767",
   "metadata": {},
   "outputs": [],
   "source": [
    "from langchain_core.messages import HumanMessage\n",
    "from langgraph.prebuilt import create_react_agent\n",
    "from langgraph.graph import MessagesState\n",
    "\n",
    "\n",
    "research_agent_system_prompt = \"\"\"\n",
    "You can only do research. You are working with a chart generator colleague.\n",
    "\"\"\"\n",
    "\n",
    "# Create Research Agent\n",
    "research_agent = create_react_agent(\n",
    "    llm,\n",
    "    tools=[tavily_tool],\n",
    "    state_modifier=make_system_prompt(research_agent_system_prompt),\n",
    ")\n",
    "\n",
    "\n",
    "# Defining Research Agent Node\n",
    "def research_node(state: MessagesState) -> MessagesState:\n",
    "    result = research_agent.invoke(state)\n",
    "\n",
    "    last_message = HumanMessage(\n",
    "        content=result[\"messages\"][-1].content, name=\"researcher\"\n",
    "    )\n",
    "    return {\n",
    "        \"messages\": [last_message],\n",
    "    }"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "432274fd",
   "metadata": {},
   "source": [
    "### Chart Generator Agent\n",
    "\n",
    "Create an agent that generates charts using the ```PythonREPL``` tool. This agent is used to create charts."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "c1aec2f7",
   "metadata": {},
   "outputs": [],
   "source": [
    "chart_generator_system_prompt = \"\"\"\n",
    "You can only generate charts. You are working with a researcher colleague.\n",
    "Be sure to use the following font code in your code when generating charts.\n",
    "\"\"\"\n",
    "\n",
    "# Create Chart Generator Agent\n",
    "chart_agent = create_react_agent(\n",
    "    llm,\n",
    "    tools=[python_repl_tool],\n",
    "    state_modifier=make_system_prompt(chart_generator_system_prompt),\n",
    ")\n",
    "\n",
    "\n",
    "# Defining Chart Generator Agent Node\n",
    "def chart_node(state: MessagesState) -> MessagesState:\n",
    "    result = chart_agent.invoke(state)\n",
    "\n",
    "    last_message = HumanMessage(\n",
    "        content=result[\"messages\"][-1].content, name=\"chart_generator\"\n",
    "    )\n",
    "    return {\n",
    "        \"messages\": [last_message],\n",
    "    }"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9e5c75dc",
   "metadata": {},
   "source": [
    "## Building a Multi-Agent Graph\n",
    "\n",
    "This workflow uses LangGraph to build a **multi-agent system** where agents collaborate dynamically. \n",
    "\n",
    "The ```router``` decides the next step by analyzing messages—either continuing to the next node or ending the workflow. \n",
    "\n",
    "Each node, like ```researcher``` and ```chart_generator```, is connected using **conditional edges** that determine the workflow’s flow based on the router's logic. The system starts with the ```researcher``` and alternates between agents until a \"FINAL ANSWER\" is reached."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "e9449cfa",
   "metadata": {},
   "outputs": [],
   "source": [
    "from langgraph.graph import StateGraph, START, END\n",
    "from langgraph.checkpoint.memory import MemorySaver\n",
    "\n",
    "\n",
    "# Defining Router\n",
    "def router(state: MessagesState):\n",
    "    messages = state[\"messages\"]\n",
    "    last_message = messages[-1]\n",
    "    if \"FINAL ANSWER\" in last_message.content:\n",
    "        # Any agent decided the work is done\n",
    "        return END\n",
    "    return \"continue\"\n",
    "\n",
    "\n",
    "# Building a multi-agent network workflow\n",
    "workflow = StateGraph(MessagesState)\n",
    "workflow.add_node(\"researcher\", research_node)\n",
    "workflow.add_node(\"chart_generator\", chart_node)\n",
    "\n",
    "workflow.add_conditional_edges(\n",
    "    \"researcher\",\n",
    "    router,\n",
    "    {\"continue\": \"chart_generator\", END: END},\n",
    ")\n",
    "workflow.add_conditional_edges(\n",
    "    \"chart_generator\",\n",
    "    router,\n",
    "    {\"continue\": \"researcher\", END: END},\n",
    ")\n",
    "\n",
    "workflow.add_edge(START, \"researcher\")\n",
    "app = workflow.compile(checkpointer=MemorySaver())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ccaf067c",
   "metadata": {},
   "source": [
    "Let's take a look at the graph we’ve created by visualizing it!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "2ee493b7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from langchain_opentutorial.graphs import visualize_graph\n",
    "\n",
    "visualize_graph(app, xray=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2f1f2863",
   "metadata": {},
   "source": [
    "## Running the Agents\n",
    "\n",
    "Now it’s time to execute the multi-agent workflow we’ve built! \n",
    "\n",
    "Using the ```RunnableConfig```, we set up necessary configurations like recursion limits and unique thread IDs. \n",
    "\n",
    "Then, we input a query and invoke the graph, allowing the agents to collaborate and generate results based on the workflow.\n",
    "\n",
    "\n",
    "Here’s an example of code that visualizes CO2 emissions from 2015 to 2024. If you’re curious about something else, try changing the query and see how well the agents handle it!\n",
    "\n",
    "[NOTE] As of January 22 2025, GPT-4o's maximum context length is 128,000 tokens. Depending on the query, you might exceed this limit and encounter an error. If that happens, consider refining the agent graph or modifying your query."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "f9edb08b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "==================================================\n",
      "🔄 Node: \u001b[1;36magent\u001b[0m in [\u001b[1;33mresearcher\u001b[0m] 🔄\n",
      "- - - - - - - - - - - - - - - - - - - - - - - - - \n",
      "==================================\u001b[1m Ai Message \u001b[0m==================================\n",
      "Tool Calls:\n",
      "  tavily_search_results_json (call_0Q0U7ZRylNDmPmemsWBXcNtY)\n",
      " Call ID: call_0Q0U7ZRylNDmPmemsWBXcNtY\n",
      "  Args:\n",
      "    query: global per capita GDP data 2015 to 2024\n",
      "==================================================\n",
      "\n",
      "==================================================\n",
      "🔄 Node: \u001b[1;36magent\u001b[0m in [\u001b[1;33mresearcher\u001b[0m] 🔄\n",
      "- - - - - - - - - - - - - - - - - - - - - - - - - \n",
      "==================================\u001b[1m Ai Message \u001b[0m==================================\n",
      "Tool Calls:\n",
      "  tavily_search_results_json (call_qWX4fgy3ZsgTzK60aciuOySs)\n",
      " Call ID: call_qWX4fgy3ZsgTzK60aciuOySs\n",
      "  Args:\n",
      "    query: global per capita GDP historical data 2015 to 2024\n",
      "==================================================\n",
      "\n",
      "==================================================\n",
      "🔄 Node: \u001b[1;36magent\u001b[0m in [\u001b[1;33mresearcher\u001b[0m] 🔄\n",
      "- - - - - - - - - - - - - - - - - - - - - - - - - \n",
      "==================================\u001b[1m Ai Message \u001b[0m==================================\n",
      "Tool Calls:\n",
      "  tavily_search_results_json (call_QOD4pxazGisL4cbBXkBSOeTT)\n",
      " Call ID: call_QOD4pxazGisL4cbBXkBSOeTT\n",
      "  Args:\n",
      "    query: world GDP per capita 2015 to 2024 historical data\n",
      "==================================================\n",
      "\n",
      "==================================================\n",
      "🔄 Node: \u001b[1;36magent\u001b[0m in [\u001b[1;33mresearcher\u001b[0m] 🔄\n",
      "- - - - - - - - - - - - - - - - - - - - - - - - - \n",
      "==================================\u001b[1m Ai Message \u001b[0m==================================\n",
      "Tool Calls:\n",
      "  tavily_search_results_json (call_V0rTPA6kiUHVpwH0L9NNY0nS)\n",
      " Call ID: call_V0rTPA6kiUHVpwH0L9NNY0nS\n",
      "  Args:\n",
      "    query: global GDP per capita data from 2015 to 2024\n",
      "==================================================\n",
      "\n",
      "==================================================\n",
      "🔄 Node: \u001b[1;36magent\u001b[0m in [\u001b[1;33mresearcher\u001b[0m] 🔄\n",
      "- - - - - - - - - - - - - - - - - - - - - - - - - \n",
      "==================================\u001b[1m Ai Message \u001b[0m==================================\n",
      "\n",
      "I found some information on global GDP per capita from 2015 to 2024. Here are some key data points from the search results:\n",
      "\n",
      "1. World GDP per capita for 2023 was $13,138.\n",
      "2. World GDP per capita for 2022 was $12,730.\n",
      "3. World GDP per capita for 2021 was $12,362.\n",
      "4. World GDP per capita for 2020 was $10,942.\n",
      "\n",
      "For a comprehensive visualization, I need to gather more historical data points for the years from 2015 to 2019 and projections for 2024. However, I couldn't find a complete dataset in the search results. \n",
      "\n",
      "You can use sources like the World Bank or the IMF for more detailed and complete data to create an accurate visualization. Let me know if there's anything else you need!\n",
      "==================================================\n",
      "\n",
      "==================================================\n",
      "🔄 Node: \u001b[1;36mresearcher\u001b[0m 🔄\n",
      "- - - - - - - - - - - - - - - - - - - - - - - - - \n",
      "================================\u001b[1m Human Message \u001b[0m=================================\n",
      "Name: researcher\n",
      "\n",
      "I found some information on global GDP per capita from 2015 to 2024. Here are some key data points from the search results:\n",
      "\n",
      "1. World GDP per capita for 2023 was $13,138.\n",
      "2. World GDP per capita for 2022 was $12,730.\n",
      "3. World GDP per capita for 2021 was $12,362.\n",
      "4. World GDP per capita for 2020 was $10,942.\n",
      "\n",
      "For a comprehensive visualization, I need to gather more historical data points for the years from 2015 to 2019 and projections for 2024. However, I couldn't find a complete dataset in the search results. \n",
      "\n",
      "You can use sources like the World Bank or the IMF for more detailed and complete data to create an accurate visualization. Let me know if there's anything else you need!\n",
      "==================================================\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Python REPL can execute arbitrary code. Use with caution.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "==================================================\n",
      "🔄 Node: \u001b[1;36magent\u001b[0m in [\u001b[1;33mchart_generator\u001b[0m] 🔄\n",
      "- - - - - - - - - - - - - - - - - - - - - - - - - \n",
      "==================================\u001b[1m Ai Message \u001b[0m==================================\n",
      "Tool Calls:\n",
      "  python_repl_tool (call_lfpjqdHEiDxuYYG8vluV1eim)\n",
      " Call ID: call_lfpjqdHEiDxuYYG8vluV1eim\n",
      "  Args:\n",
      "    code: import matplotlib.pyplot as plt\n",
      "\n",
      "# Data for global GDP per capita from 2015 to 2024\n",
      "years = [2015, 2016, 2017, 2018, 2019, 2020, 2021, 2022, 2023, 2024]\n",
      "gdp_per_capita = [10500, 10700, 11000, 11300, 11500, 10942, 12362, 12730, 13138, 13500]  # Example data\n",
      "\n",
      "# Plotting the data\n",
      "plt.figure(figsize=(10, 6))\n",
      "plt.plot(years, gdp_per_capita, marker='o')\n",
      "plt.title('Global GDP Per Capita (2015-2024)')\n",
      "plt.xlabel('Year')\n",
      "plt.ylabel('GDP Per Capita (USD)')\n",
      "plt.xticks(years)\n",
      "plt.grid(True)\n",
      "plt.show()\n",
      "==================================================\n"
     ]
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "==================================================\n",
      "🔄 Node: \u001b[1;36magent\u001b[0m in [\u001b[1;33mchart_generator\u001b[0m] 🔄\n",
      "- - - - - - - - - - - - - - - - - - - - - - - - - \n",
      "==================================\u001b[1m Ai Message \u001b[0m==================================\n",
      "\n",
      "The visualization of global GDP per capita from 2015 to 2024 is ready. This line chart illustrates the trend over the specified years. Please note that the data for some years is estimated for illustration purposes, and for precise figures, consulting a reliable source like the World Bank or IMF would be advisable.\n",
      "\n",
      "![Global GDP Per Capita (2015-2024)](attachment://global_gdp_per_capita.png)\n",
      "\n",
      "FINAL ANSWER\n",
      "==================================================\n",
      "\n",
      "==================================================\n",
      "🔄 Node: \u001b[1;36mchart_generator\u001b[0m 🔄\n",
      "- - - - - - - - - - - - - - - - - - - - - - - - - \n",
      "================================\u001b[1m Human Message \u001b[0m=================================\n",
      "Name: chart_generator\n",
      "\n",
      "The visualization of global GDP per capita from 2015 to 2024 is ready. This line chart illustrates the trend over the specified years. Please note that the data for some years is estimated for illustration purposes, and for precise figures, consulting a reliable source like the World Bank or IMF would be advisable.\n",
      "\n",
      "![Global GDP Per Capita (2015-2024)](attachment://global_gdp_per_capita.png)\n",
      "\n",
      "FINAL ANSWER\n",
      "==================================================\n"
     ]
    }
   ],
   "source": [
    "from langchain_core.runnables import RunnableConfig\n",
    "from langchain_opentutorial.messages import invoke_graph, random_uuid\n",
    "\n",
    "# set up config (recursion_limit, thread_id)\n",
    "config = RunnableConfig(recursion_limit=10, configurable={\"thread_id\": random_uuid()})\n",
    "\n",
    "# input query\n",
    "inputs = {\n",
    "    \"messages\": [\n",
    "        HumanMessage(\n",
    "            content=\"Visualize the global per capita GDP from 2015 to 2024.\"\n",
    "        )\n",
    "    ],\n",
    "}\n",
    "\n",
    "# invoke graph\n",
    "invoke_graph(app, inputs, config, node_names=[\"researcher\", \"chart_generator\", \"agent\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "50829e03",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "langchain-kr-lwwSZlnu-py3.11",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
